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Abstract

Background: One of the leading causes of Congestive Heart Failure (CHF) is cardiomyopathy, a disorder with a high heritable component. There are few clinical clues to guide genetic diagnosis, so genetic testing for familial cardiomyopathy relies on panel-based screening which may survey as many as 46 responsible genes. Recently the large gene TTN, comprising 350 exons, was identified as the cause of nearly 25% of dilated cardiomyopathy (DCM). Even with the inclusion of the gene TTN in gene panels for cardiomyopathy, the sensitivity of testing for DCM is only ∼45%. Motivated both by the large number of genes required to diagnose genetic cardiomyopathy and improvements in high-throughput sequencing technology, we piloted whole genome sequencing (WGS) for 11 subjects with DCM.

Methods: WGS was performed to achieve an average of 35 fold coverage using paired end 100bp reads. We focused our analysis on 212 genes linked to both syndromic and nonsyndromic cardiomyopathy as well as genes identified from animal modeling. Variants within these genes were analyzed using a pipeline that combined multiple prediction algorithms along with frequency data from the 1000Genomes project and the NHLBI Exome Sequencing Project. This pipeline yielded 3-15 potentially pathogenic variants per individual. Variants were further analyzed using clinical criteria and confirmed using traditional Sanger sequencing. When available, segregation in family members was analyzed.

Results: Three of 11 subjects within the DCM cohort had known primary mutations, and these three mutations were detected by the analysis pipeline. In five of eight subjects for whom the primary mutation was unknown, we identified mutations that either segregated with disease, had clinical correlates, and/or had additional pathological correlation to provide evidence for causality. For at least two subjects, we identified additional variants that may be acting as modifiers of disease severity. In total, we identified the likely pathological mutation in 8 of 11 (73%) subjects.

Conclusion: Cardiomyopathy is a genetically heterogeneous disease that lends itself to analysis by WGS. WGS can be used to identify clinically relevant variants of both therapeutic and prognostic importance.